--- license: mit tags: - generated_from_trainer model-index: - name: rubert-tiny2-srl results: [] --- # rubert-tiny2-srl This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3357 - Benefactive Precision: 1.0 - Benefactive Recall: 0.5 - Benefactive F1: 0.6667 - Benefactive Number: 2 - Causator Precision: 1.0 - Causator Recall: 1.0 - Causator F1: 1.0 - Causator Number: 12 - Cause Precision: 0.4 - Cause Recall: 0.4 - Cause F1: 0.4000 - Cause Number: 5 - Contrsubject Precision: 0.75 - Contrsubject Recall: 0.6667 - Contrsubject F1: 0.7059 - Contrsubject Number: 9 - Deliberative Precision: 1.0 - Deliberative Recall: 1.0 - Deliberative F1: 1.0 - Deliberative Number: 4 - Experiencer Precision: 0.7442 - Experiencer Recall: 0.8101 - Experiencer F1: 0.7758 - Experiencer Number: 79 - Object Precision: 0.7551 - Object Recall: 0.7450 - Object F1: 0.7500 - Object Number: 149 - Predicate Precision: 0.9809 - Predicate Recall: 0.9885 - Predicate F1: 0.9847 - Predicate Number: 260 - Overall Precision: 0.8705 - Overall Recall: 0.8788 - Overall F1: 0.8746 - Overall Accuracy: 0.9411 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00010372880304918982 - train_batch_size: 1 - eval_batch_size: 1 - seed: 923789 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.29 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Benefactive Precision | Benefactive Recall | Benefactive F1 | Benefactive Number | Causator Precision | Causator Recall | Causator F1 | Causator Number | Cause Precision | Cause Recall | Cause F1 | Cause Number | Contrsubject Precision | Contrsubject Recall | Contrsubject F1 | Contrsubject Number | Deliberative Precision | Deliberative Recall | Deliberative F1 | Deliberative Number | Experiencer Precision | Experiencer Recall | Experiencer F1 | Experiencer Number | Object Precision | Object Recall | Object F1 | Object Number | Predicate Precision | Predicate Recall | Predicate F1 | Predicate Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.3041 | 1.0 | 4864 | 0.3394 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 0.4167 | 0.5882 | 12 | 0.0 | 0.0 | 0.0 | 5 | 1.0 | 0.1111 | 0.2000 | 9 | 0.0 | 0.0 | 0.0 | 4 | 0.8372 | 0.4557 | 0.5902 | 79 | 0.8730 | 0.3691 | 0.5189 | 149 | 0.9884 | 0.9846 | 0.9865 | 260 | 0.9515 | 0.6788 | 0.7924 | 0.9141 | | 0.3178 | 2.0 | 9728 | 0.2692 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 0.2 | 0.3333 | 5 | 1.0 | 0.2222 | 0.3636 | 9 | 1.0 | 0.5 | 0.6667 | 4 | 0.7403 | 0.7215 | 0.7308 | 79 | 0.8523 | 0.5034 | 0.6329 | 149 | 0.9808 | 0.9846 | 0.9827 | 260 | 0.9142 | 0.7788 | 0.8411 | 0.9321 | | 0.124 | 3.0 | 14592 | 0.2990 | 1.0 | 0.5 | 0.6667 | 2 | 1.0 | 1.0 | 1.0 | 12 | 0.0 | 0.0 | 0.0 | 5 | 0.75 | 0.6667 | 0.7059 | 9 | 1.0 | 0.5 | 0.6667 | 4 | 0.7386 | 0.8228 | 0.7784 | 79 | 0.8 | 0.6980 | 0.7455 | 149 | 0.9885 | 0.9885 | 0.9885 | 260 | 0.8904 | 0.8596 | 0.8748 | 0.9435 | | 0.104 | 4.0 | 19456 | 0.2852 | 1.0 | 0.5 | 0.6667 | 2 | 0.9231 | 1.0 | 0.9600 | 12 | 0.4286 | 0.6 | 0.5 | 5 | 0.6 | 0.6667 | 0.6316 | 9 | 1.0 | 0.75 | 0.8571 | 4 | 0.7253 | 0.8354 | 0.7765 | 79 | 0.7044 | 0.7517 | 0.7273 | 149 | 0.9847 | 0.9885 | 0.9866 | 260 | 0.8440 | 0.8846 | 0.8638 | 0.9359 | | 0.0918 | 5.0 | 24320 | 0.3357 | 1.0 | 0.5 | 0.6667 | 2 | 1.0 | 1.0 | 1.0 | 12 | 0.4 | 0.4 | 0.4000 | 5 | 0.75 | 0.6667 | 0.7059 | 9 | 1.0 | 1.0 | 1.0 | 4 | 0.7442 | 0.8101 | 0.7758 | 79 | 0.7551 | 0.7450 | 0.7500 | 149 | 0.9809 | 0.9885 | 0.9847 | 260 | 0.8705 | 0.8788 | 0.8746 | 0.9411 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3